ranger() survival models now also work out-of-the-box
without passing a tailored prediction function. Use the new argument
survival = "chf" in hstats(),
ice(), and partial_dep() to distinguish
cumulative hazards (default) and survival probabilities (“prob”) per
time point.swap_dim = TRUE for
the old behavior.predict_type = "prob".X (#109).0/0 (= NaN). Such values are now replaced by 0
(#107).d2_geom = "line". Instead of a heatmap of the two features,
one of the features is moved to color grouping. Combined with
swap_dim = TRUE, you can swap the role of the two
v variables without recalculating anything. The idea was
proposed by Roel Verbelen
in issue
#91, see also issue
#94.BY and w via column names would fail
for tibbles. This problem was described in #92 by Roel Verbelen. Thx!perm_importance() and average_loss()).hstats() now has the option
approx = FALSE. Set to TRUE to replace values
of dense numeric columns by grid_size = 50 quantile
midpoints. This will bring a massive speed-up for one-way calculations.
Use this option when one-way calculations are slow, or when you want to
increase n_max.hstats(): n_max has been increased from
300 to 500 rows. This will make estimates of H-statistics more stable at
the price of longer run time. Reduce to 300 for the old behaviour.hstats(): Three-way interactions are not anymore
calculated by default. Set threeway_m to 5 for the old
behaviour.options(hstats.fill = new value). Value labels are more
clear, and there are more options. Varying color/fill scales now use
viridis (inferno). This can be modified on the fly or via
options(hstats.viridis_args = list(...)).h2_pairwise() or perm_importance(), now return
a “hstats_matrix”. The values are stored in $M and can be
plotted via plot(). Other methods include:
dimnames(), rownames(),
colnames(), dim(), nrow(),
ncol(), head(), tail(), and
subsetting like a normal matrix. This allows, e.g, to select and plot
only one column of the results.perm_importance(): The perms argument has
been changed to m_rep.print() and summary() methods have been
revised.w (case weights) and y
(response) can now also be passed as column names.top_m has been moved to the
plot() method.eps of squared
numerator statistics has been reduced from 1e-8 to
1e-10. It is now handled in hstats() instead
of the statistic functions.H-squared: The \(H^2\)
statistic stored in a “hstats” object is now a matrix with one row (it
was a vector).pd_importance(): The “hstats” object now contains
pre-calculated PD-based importance values in
$pd_importance.summary.hstats() now returns an object of class
“hstats_summary” instead of “summary_hstats”.average_loss() is more flexible regarding the group
BY argument. It can also be a variable name.
Non-discrete BY variables are now automatically binned.
Like partial_dep(), binning is controlled by the
by_size = 4 argument.average_loss() also returns a “hstats_matrix” object
with print() and plot() method. The values can
be extracted via $M.v of hstats() and
perm_importance() is now NULL. Internally, it
is set to colnames(X) (minus the column names of
w and y if passed as name).partial_dep() and
ice() have received a na.rm argument that
controls if missing values are dropped during grid creation. The default
TRUE is compatible with earlier releases.hstats(): Discrete variables with
missings would cause rowsum() to launch repeated warnings.
This case is now catched.perm_importance(): The default of verbose
is TRUE again.This is intended to be the last version before 1.0.0.
ice() and partial_dep(): So far,
the default grid strategy “uniform” used pretty() to
generate the evaluation points. To provide more predictable grid sizes,
and to be more in line with other implementations of partial dependence
and ICE, we now use seq() to create the uniform grid.h2_pairwise() and h2_threeway() will now
also include 0 values. Use zero = FALSE to drop them, see
below. The padding with 0 is done at no computational cost, and will
affect only up to pairwise_m and threeway_m
features.print() method of summary.hstats() is
less verbose.h2_overall(), h2_pairwise(),
h2_threeway(), plot.hstats(), and
summary.hstats() have received an argument
zero = TRUE. Set to FALSE to drop statistics
having value 0.perm_importance() and average_loss() will
now recycle a univariate response when combined with multivariate
predictions. This is useful, e.g., when the prediction function
represents the predictions of multiple models that should be evaluated
against a common response.perm_importance() and average_loss() would
fail for “mlogloss” in case the response y was univariate
and non-factor/non-character.hstats() and attached to the resulting object. Each
statistic is stored as list with numerator and denominator
matrices/vectors. The functions h2(),
h2_overall(), h2_pairwise(), and
h2_threeway(), print.hstats(),
summary().hstats(), plot.hstats() will use
these without having to recalculate the required numerators and
denominators. The results, however, are unchanged.average_loss(): This new function calculates the
average loss of a model for a given dataset, optionally grouped by a
discrete vector. It supports the most important loss functions (squared
error, Poisson deviance, Gamma deviance, Log loss, multivariate Log
loss, absolute error, classification error), and allows for case
weights. Custom losses can be passed as vector/matrix valued functions
of signature f(obs, pred). Note that such a custom function
needs to return per-row losses, not their average.
perm_importance(): H-statistics are often
calculated for important features only. To support this workflow, we
have added permutation importance regarding the most important loss
functions. Multivariate losses can be studied individually or collapsed
over dimensions. The importance of feature groups can be
studied as well. Note that the API of perm_importance() is
different from the experimental pd_importance(), which is
calculated from a “hstats” object.
hstats() now uses the default feature vector
v = colnames(X), simplifying the API in most cases. The
typical call is now hstats(object, X = Feature data).h2_overall(), h2_pairwise(),
h2_threeway(), pd_importance() by default do
not plot results anymore. Set plot = TRUE to do so.summary.hstats() now returns an object of class
“summary_hstats” with its own print() method. Like this,
one can use su <- summary() without printing to the
console.summary.hstats() is printed slightly more
compact.plot.hstats() has recieved a
rotate_x = FALSE argument for rotating x labels by 45
degrees.plot.hstats() and summary.hstats() have
received explicit arguments normalize,
squared, sort, eps instead of
passing them via ....plot.hstats() now passes ... to
geom_bar().hstats() in the one-dimensional
case.This is the initial release.